Patents by Inventor David Samuel Friedlander

David Samuel Friedlander has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11386666
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: September 28, 2018
    Date of Patent: July 12, 2022
    Assignee: AVIGILON PATENT HOLDING 1 CORPORATION
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal
  • Publication number: 20190034737
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Application
    Filed: September 28, 2018
    Publication date: January 31, 2019
    Inventors: Wesley Kenneth COBB, David Samuel FRIEDLANDER, Kishor Adinath SAITWAL
  • Patent number: 10121077
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: December 20, 2012
    Date of Patent: November 6, 2018
    Assignee: AVIGILON PATENT HOLDING 1 CORPORATION
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal
  • Patent number: 8797405
    Abstract: Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification.
    Type: Grant
    Filed: August 31, 2009
    Date of Patent: August 5, 2014
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Patent number: 8620028
    Abstract: Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
    Type: Grant
    Filed: March 6, 2012
    Date of Patent: December 31, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis Gene Urech, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Lon William Risinger, Kishor Adinath Saitwal, Ming-Jung Seow, David Marvin Solum, Gang Xu, Tao Yang
  • Patent number: 8493409
    Abstract: Techniques are disclosed for visually conveying a sequence storing an ordered string of symbols generated from kinematic data derived from analyzing an input stream of video frames depicting one or more foreground objects. The sequence may represent information learned by a video surveillance system. A request may be received to view the sequence or a segment partitioned form the sequence. A visual representation of the segment may be generated and superimposed over a background image associated with the scene. A user interface may be configured to display the visual representation of the sequence or segment and to allow a user to view and/or modify properties associated with the sequence or segment.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: July 23, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • Patent number: 8379085
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: February 19, 2013
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal
  • Patent number: 8340352
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: December 25, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, David Samuel Friedlander, Kishor Adinath Saitwal
  • Patent number: 8295591
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: October 23, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Kishor Adinath Saitwal, Gang Xu
  • Patent number: 8280153
    Abstract: Techniques are disclosed for visually conveying a trajectory map. The trajectory map provides users with a visualization of data observed by a machine-learning engine of a behavior recognition system. Further, the visualization may provide an interface used to guide system behavior. For example, the interface may be used to specify that the behavior recognition system should alert (or not alert) when a particular trajectory is observed to occur.
    Type: Grant
    Filed: August 18, 2009
    Date of Patent: October 2, 2012
    Assignee: Behavioral Recognition Systems
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Ming-Jung Seow, Gang Xu
  • Publication number: 20120163670
    Abstract: Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
    Type: Application
    Filed: March 6, 2012
    Publication date: June 28, 2012
    Applicant: BEHAVIORAL RECOGNITION SYSTEMS, INC.
    Inventors: John Eric EATON, Wesley Kenneth COBB, Dennis Gene URECH, Bobby Ernest BLYTHE, David Samuel FRIEDLANDER, Rajkiran Kumar GOTTUMUKKAL, Lon William RISINGER, Kishor Adinath SAITWAL, Ming-Jung SEOW, David Marvin SOLUM, Gang XU, Tao YANG
  • Patent number: 8131012
    Abstract: Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.
    Type: Grant
    Filed: February 8, 2008
    Date of Patent: March 6, 2012
    Assignee: BEHAVIORAL RECOGNITION SYSTEMS, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Dennis Gene Urech, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Lon William Risinger, Kishor Adinath Saitwal, Ming-Jung Seow, David Marvin Solum, Gang Xu, Tao Yang
  • Patent number: 8121968
    Abstract: A long-term memory used to store and retrieve information learned while a video analysis system observes a stream of video frames is disclosed. The long-term memory provides a memory with a capacity that grows in size gracefully, as events are observed over time. Additionally, the long-term memory may encode events, represented by sub-graphs of a neural network. Further, rather than predefining a number of patterns recognized and manipulated by the long-term memory, embodiments of the invention provide a long-term memory where the size of a feature dimension (used to determine the similarity between different observed events) may grow dynamically as necessary, depending on the actual events observed in a sequence of video frames.
    Type: Grant
    Filed: September 11, 2008
    Date of Patent: February 21, 2012
    Assignee: Behavioral Recognition Systems, Inc.
    Inventors: John Eric Eaton, Wesley Kenneth Cobb, Ming-Jung Seow, David Samuel Friedlander, Gang Xu
  • Publication number: 20110050897
    Abstract: Techniques are disclosed for visually conveying classifications derived from pixel-level micro-features extracted from image data. The image data may include an input stream of video frames depicting one or more foreground objects. The classifications represent information learned by a video surveillance system. A request may be received to view a classification. A visual representation of the classification may be generated. A user interface may be configured to display the visual representation of the classification and to allow a user to view and/or modify properties associated with the classification.
    Type: Application
    Filed: August 31, 2009
    Publication date: March 3, 2011
    Inventors: Wesley Kenneth Cobb, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal, Ming-Jung Seow, Gang Xu
  • Publication number: 20110044492
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Application
    Filed: August 18, 2009
    Publication date: February 24, 2011
    Inventors: WESLEY KENNETH COBB, Bobby Ernest Blythe, David Samuel Friedlander, Kishor Adinath Saitwal, Gang Xu
  • Publication number: 20110043536
    Abstract: Techniques are disclosed for visually conveying a sequence storing an ordered string of symbols generated from kinematic data derived from analyzing an input stream of video frames depicting one or more foreground objects. The sequence may represent information learned by a video surveillance system. A request may be received to view the sequence or a segment partitioned form the sequence. A visual representation of the segment may be generated and superimposed over a background image associated with the scene. A user interface may be configured to display the visual representation of the sequence or segment and to allow a user to view and/or modify properties associated with the sequence or segment.
    Type: Application
    Filed: August 18, 2009
    Publication date: February 24, 2011
    Inventors: WESLEY KENNETH COBB, Bobby Ernest Blythe, David Samuel Friedlander, Rajkiran Kumar Gottumukkal, Kishor Adinath Saitwal
  • Publication number: 20110043626
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Application
    Filed: August 18, 2009
    Publication date: February 24, 2011
    Inventors: WESLEY KENNETH COBB, David Samuel Friedlander, Kishor Adinath Saitwal
  • Publication number: 20110044498
    Abstract: Techniques are disclosed for visually conveying a trajectory map. The trajectory map provides users with a visualization of data observed by a machine-learning engine of a behavior recognition system. Further, the visualization may provide an interface used to guide system behavior. For example, the interface may be used to specify that the behavior recognition system should alert (or not alert) when a particular trajectory is observed to occur.
    Type: Application
    Filed: August 18, 2009
    Publication date: February 24, 2011
    Inventors: WESLEY KENNETH COBB, BOBBY ERNEST BLYTHE, DAVID SAMUEL FRIEDLANDER, RAJKRAN KUMAR GOTTUMUKKAL, MING-JENG SEOW, GANG XU
  • Publication number: 20110044499
    Abstract: A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.
    Type: Application
    Filed: August 18, 2009
    Publication date: February 24, 2011
    Inventors: WESLEY KENNETH COBB, David Samuel Friedlander, Kishor Adinath Saitwal
  • Publication number: 20100063949
    Abstract: A long-term memory used to store and retrieve information learned while a video analysis system observes a stream of video frames is disclosed. The long-term memory provides a memory with a capacity that grows in size gracefully, as events are observed over time. Additionally, the long-term memory may encode events, represented by sub-graphs of a neural network. Further, rather than predefining a number of patterns recognized and manipulated by the long-term memory, embodiments of the invention provide a long-term memory where the size of a feature dimension (used to determine the similarity between different observed events) may grow dynamically as necessary, depending on the actual events observed in a sequence of video frames.
    Type: Application
    Filed: September 11, 2008
    Publication date: March 11, 2010
    Inventors: JOHN ERIC EATON, Wesley Kenneth Cobb, Ming-Jung Seow, David Samuel Friedlander, Gang Xu